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Full Critical Review

Large-scale metal additive manufacturing: a holistic review of the state of the art and challenges

ORCID Icon, , , , ORCID Icon, , & show all
Received 17 Jun 2020
Accepted 08 Aug 2021
Published online: 13 Oct 2021

ABSTRACT

Additive Manufacturing (AM) has the potential to completely reshape the manufacturing space by removing the geometrical constraints of commercial manufacturing and reducing component lead time, especially for large-scale parts. Coupling robotic systems with direct energy deposition (DED) additive manufacturing techniques allow for support-free printing of parts where part sizes are scalable from sub-metre to multi-metre sizes. This paper offers a holistic review of large-scale robotic additive manufacturing, beginning with an introduction to AM, followed by different DED techniques, the compatible materials and their typical as-built microstructures. Next, the multitude of robotic build platforms that extend the deposition from the standard 2.5 degrees of freedom (DOF) to 6 and 8 DOF is discussed. With this context, the decomposition and slicing of the computerized model will be described, and the challenges of planning the deposition trajectory will be discussed. The different modalities to monitor and control the deposition in an attempt to meet the geometrical and performance specifications are outlined and discussed. A wide range of metals and alloys have been reported and evaluated for large-scale AM parts. These include steels, Ti, Al, Mg, Cu, Ni, Co–Cr and W alloys. Different post-processing steps, including heat treatments, are discussed, along with their microstructures. This paper finally addresses the authors' perspective on the future of the field and the largest knowledge gaps that need to be filled before the commercial implementation of robotic AM.

Acknowledgments

This work would not be possible without the financial aid from the Syncrude-NSERC CRD (CRDPJ 514752-17), Mitacs (MITACS MA IT11329) and HI-AM (NSERC HI-AM NETGP 494158), as well as the in-kind contribution and resources given by Innotech Alberta. The authors would like to thank Stefano Chiovelli for his support and adding an industrial perspective to the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by 10.13039/501100004489Mitacs[MITACS MA IT11329]NSERC HI-AM[NSERC HI-AM NETGP 494158]Syncrude-NSERC CRD[CRDPJ 514752-17].

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